{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "╒═════╤══════════════════╤═══════════════════╤════════════╕\n",
      "│   # │   measure_values │   estimate_values │   K_values │\n",
      "╞═════╪══════════════════╪═══════════════════╪════════════╡\n",
      "│   0 │               81 │           78.75   │  0.625     │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   1 │               83 │           80.3846 │  0.384615  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   2 │               79 │           80      │  0.277778  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   3 │               78 │           79.5652 │  0.217391  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   4 │               81 │           79.8214 │  0.178571  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   5 │               79 │           79.697  │  0.151515  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   6 │               80 │           79.7368 │  0.131579  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   7 │               78 │           79.5349 │  0.116279  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   8 │               81 │           79.6875 │  0.104167  │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│   9 │               79 │           79.6226 │  0.0943396 │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│  10 │               80 │           79.6552 │  0.0862069 │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│  11 │               78 │           79.5238 │  0.0793651 │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│  12 │               81 │           79.6324 │  0.0735294 │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│  13 │               79 │           79.589  │  0.0684932 │\n",
      "├─────┼──────────────────┼───────────────────┼────────────┤\n",
      "│  14 │               82 │           79.7436 │  0.0641026 │\n",
      "╘═════╧══════════════════╧═══════════════════╧════════════╛\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "import numpy as np\n",
    "from tabulate import tabulate\n",
    "measure_count = 15\n",
    "measure_values = [81,83,79,78,81,79,80,78,81,79,80,78,81,79,82]\n",
    "measure_error = [3] * 15\n",
    "pre_estimate_value = 75\n",
    "pre_estimate_error = 5\n",
    "estimate_values = np.zeros(measure_count)\n",
    "estimate_errors = np.zeros(measure_count)\n",
    "K_values = np.zeros(measure_count)\n",
    "\n",
    "for i in range(measure_count):\n",
    "    # K_n = \\frac{最优估计值误差_{n_1}}{最优估计值误差_{n_1} + 测量值误差_n}\n",
    "    K_values[i] = pre_estimate_error/(pre_estimate_error + measure_error[i])\n",
    "    # 最优估计值_n = 最优估计值_{n-1} + K_n \\times (测量值_n - 最优估计值_{n-1})\n",
    "    estimate_values[i] = pre_estimate_value + K_values[i] * (measure_values[i] - pre_estimate_value)\n",
    "    # 最优估计值误差_{n-1} = (1 - K_{n-1}) \\times 最优估计值误差_{n-2}\n",
    "    estimate_errors[i] = (1 - K_values[i]) * pre_estimate_error\n",
    "\n",
    "    pre_estimate_value = estimate_values[i]\n",
    "    pre_estimate_error = estimate_errors[i]\n",
    "    \n",
    "#print(\"K_values=\",K_values)\n",
    "#print(\"estimate_values=\", estimate_values)\n",
    "#print(\"estimate_errors=\", estimate_errors)\n",
    "\n",
    "table = [['#', 'measure_values', 'estimate_values', \"K_values\"]]\n",
    "for i in range(measure_count):\n",
    "    table.append([i, measure_values[i], estimate_values[i], K_values[i]])\n",
    "print(tabulate(table, headers='firstrow', tablefmt='fancy_grid'))\n",
    "\n",
    "fig = plt.figure(figsize=[9,5])\n",
    "ax = plt.axes()\n",
    "\n",
    "x = np.linspace(1, 15, 15)\n",
    "_ = ax.plot(x, measure_values, label=\"measure_values\", color='red')\n",
    "_ = ax.plot(x, estimate_values, label=\"estimate_values\", color='green')\n",
    "\n",
    "_=plt.legend(loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
